The present invention relates to a process control system and a control support system using the same process control system. In particular the present invention relates to a process control system and a control support system for controlling or managing complicated processes typically found in the heat process for iron and steel making furnaces or the like, the reaction process for chemical plants or the process of injecting chemicals in the water service system, and further in the processes subjected to a sharp change in control amount such as the sump pump for sewage disposal.
In recent years, a system for measuring and controlling the conditions has been employed in many manufacturing processes and processes for controlling the public resources like the water service and sewage disposal. Typical cases include the PID control based on the error between the process conditions and a target value and an application technique for artificial intelligence in which a process operating amount is determined by inference according to an "if.about.then.about." rule using the process condition amounts.
The PID control is effective in an application having a small number of measurement information on the process. This control, however, is difficult to apply in a case involving a large number of measurement information and complicated causal relationship between the measurement information. In a technique for making inference according to the "if.about.then.about." rule, it is necessary to extract the knowhow based on the knowledge and experience of a highly skilled operator in order to build up a rule. Not only is this difficult to carry out in a; complicated process but also a trial-and-error process for the work is required in order to maintain the consistency between the different rules.
As a solution, to this problem, the present applicants propose in Japanese patent application un-examined publication No. JP-A-02-85975, a control system characterized in that features are extracted from a condition change pattern in time series and are converted into an abstract expression while using the causal relationship between pieces of the measurement information obtained by the abstract expression. This control system realizes a stabilized control overcoming various disturbances of steady process conditions accounting for a great proportion of the operating period.
Even in the above-mentioned systems, however, unsteady process conditions or sudden abnormal states have yet to be met appropriately and the control operation for restitution is required to be performed manually by human labor. The subject of introducing a system concept to this area of manual operation is considered capable of being effectively solved by employing recent achievements of technical development in applications of artificial intelligence and data analysis techniques. An application of the above-mentioned techniques to the control of an unsteady process, however, encounters the two problems mentioned below.
In the first place, it is difficult to systematize the condition identification unit representing an important element of knowhow based on the knowledge and experiences of a skilled operator. The information used by a skilled operator in the control operataion includes, in addition to the normal measurement information, the recognition of "patterns of condition change in the progress of time", "patterns of combined conditions on a plurality of measurement data", "patterns of sound, image, ambience, etc." and combinations thereof. Further, a skilled operator makes a series of decisions based on such additional factors as the non-linearity and ambiguity of this information.
In the second place, it is not an easy matter to extract a rule on the inference and decision approximate to that of a skilled operator. This problem poses a great problem in building an expert system apparently for the following reasons. A skilled worker rarely recognizes his knowledge as a definite rule. It is impossible to extract the knowledge relating to special situations not experienced by an expert.
These two reasons also apply directly to the extraction of a rule for process control.
As an effective method of rule extraction, which is also a task in applications of artifical intelligence, a unique approach is based on the of structural characteristics of an object process in the case involving the process control.